Tabular Transfer Learning and/or retraining with fastai

Deep Learning
20

My “final” version with no pandas dependencies. Pretty minimal, if I do say so myself:

import csv
import torch
from collections import OrderedDict 
from fastai.basic_train import Learner

defaultlr = 1e-3

def write_encoding_dict(filename, learner, cat_names, cat):
    classes = learner.data.label_list.train.x.classes[cat]
    weight_matrix = learner.model.embeds[cat_names.index(cat)].weight
    with open(filename, 'w') as csvFile:
        writer = csv.writer(csvFile, lineterminator='\n')
        for i in range(len(classes)):
            writer.writerow([classes[i],*weight_matrix[i].tolist()])
        
def get_encoding_dict(filename):
    with open(filename, 'r') as csvFile:
        reader = csv.reader(csvFile)
        lines = list(reader)
        d = OrderedDict()
        for i in range(len(lines)):
            d[lines[i][0]] = [float(lines[i][j]) for j in range(1,len(lines[i]))]
        return d

def load_embed_weights(filename, learner, cat_names, cat):
    encodings = get_encoding_dict(filename)
    classes = learner.data.label_list.train.x.classes[cat]
    weight_matrix = learner.model.embeds[cat_names.index(cat)].weight
    emb_dim=weight_matrix.shape[1]
    with torch.no_grad():
        for i, value in enumerate(classes):
            try: 
                enc = encodings[value]
                for j in range(emb_dim):
                    weight_matrix[i][j] = enc[j]
            except KeyError:
                for j in range(emb_dim):
                    weight_matrix[i][j] = np.random.normal(scale=0.6)
                    
def freeze_embedding(learner:Learner,index=0):
    learner.model.embeds[index].weight.requires_grad = False
    learner.create_opt(defaultlr)
                    
def unfreeze_embedding(learner:Learner,index=0):
    learner.model.embeds[index].weight.requires_grad = True
    learner.create_opt(defaultlr)
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